Cut Sets Research Articles

Mathematical Techniques for Computing the Reliability of a Parallel-series System of Order (2,3,1) under Rayleigh Failure Laws

Some well-known mathematical techniques have been used to compute the reliability of a parallel-series system of order (2,3,1). The system is composed of four units labelled as A, B, C and D. The configuration involves two parallel subsystems: the first containing units A, B and C arranged in series and the second comprising a single unit D. The failure rate of units is assumed to follow the Rayleigh distribution. With the use of the decomposition method, cut set method and event space method, expressions for both reliability and Mean Time to System Failure (MTSF) have been derived. By considering all units to be identical, the reliability measures have been obtained numerically for specific parameter values. The trends of these measures have been represented graphically to understand the effect of parameters. The study has its practical significance in assessing the reliability of solar panel system. . KEYWORDS :Parallel-series System, Rayleigh failure laws, Decomposition method, Cut set method, Event space method.

GMCSpy: efficient and accurate computation of genetic minimal cut sets in Python.

The identification of minimal genetic interventions that modulate metabolic processes constitutes one of the most relevant applications of genome-scale metabolic models (GEMs). The concept of Minimal Cut Sets (MCSs) and its extension at the gene level, genetic Minimal Cut Sets (gMCSs), have attracted increasing interest in the field of Systems Biology to address this task. Different computational tools have been developed to calculate MCSs and gMCSs using both commercial and open-source software. Here, we present gMCSpy, an efficient Python package to calculate gMCSs in GEMs using both commercial and non-commercial optimization solvers. We show that gMCSpy substantially overperforms our previous computational tool GMCS, which exclusively relied on commercial software. Moreover, we compared gMCSpy with recently published competing algorithms in the literature, finding significant improvements in both accuracy and computation time. All these advances make gMCSpy an attractive tool for researchers in the field of Systems Biology for different applications in health and biotechnology. The Python package gMCSpy and the data underlying this manuscript can be accessed at: https://github.com/PlanesLab/gMCSpy.

The combination mode of forest and SVM for power network disaster response failure identification

The damage to power transmission and distribution equipment in different regions is inseparable from the erosion of natural and artificial disasters, affecting the power grid's regular power supply and users' typical power consumption. Therefore, research on failure risk prediction technology for power transmission and distribution equipment under different disasters has increasingly become critical to power grid reliability. Based on the combination mode of forest and Support Vector Machine (SVM), this study proposes a disaster response failure prediction model that considers the time cumulative effect of disaster elements. Based on this model, the failure degree indicators of different disaster elements are constructed by analyzing the relationship between disaster frequency and time. Then, the historical disaster data is used to calculate the index and form a training set with the monitoring data. The binary decision graph transformation method is combined with the adjacent node priority method to obtain the minimum cut set, and the SVM regression method is used to train to get the prediction model. The experimental results show that the effectiveness and accuracy of the proposed method are verified through the analysis of numerical examples and comparison of various techniques. The model's accuracy is 8.63 % higher than that of the traditional disaster prediction model, and the error rate of disaster failure prediction analysis is not more than 0.01.

Systems Reliability and Data Driven Analysis for Marine Machinery Maintenance Planning and Decision Making

Understanding component criticality in machinery performance degradation is important in ensuring the reliability and availability of ship systems, particularly considering the nature of ship operations requiring extended voyage periods, usually traversing regions with multiple climate and environmental conditions. Exposing the machinery system to varying degrees of load and operational conditions could lead to rapid degradation and reduced reliability. This research proposes a tailored solution by identifying critical components, the root causes of maintenance delays, understanding the factors influencing system reliability, and recognising failure-prone components. This paper proposes a hybrid approach using reliability analysis tools and machine learning. It uses dynamic fault tree analysis (DFTA) to determine how reliable and important a system is, as well as Bayesian belief network (BBN) availability analysis to assist with maintenance decisions. Furthermore, we developed an artificial neural network (ANN) fault detection model to identify the faults responsible for system unreliability. We conducted a case study on a ship power generation system, identifying the components critical to maintenance and defects contributing to such failures. Using reliability importance measures and minimal cut sets, we isolated all faults contributing over 40% of subsystem failures and related events. Among the 4 MDGs, the lubricating system had the highest average availability of 67%, while the cooling system had the lowest at 38% using the BBN availability outcome . Therefore, the BBN DSS recommended corrective action and ConMon as maintenance strategies due to the frequent failures of certain critical parts. ANN found overheating when MDG output was above 180 kVA, linking component failure to generator performance. The findings improve ship system reliability and availability by reducing failures and improving maintenance strategies.

Convex Representation of Metabolic Networks with Michaelis–Menten Kinetics

Polyhedral models of metabolic networks are computationally tractable and can predict some cellular functions. A longstanding challenge is incorporating metabolites without losing tractability. In this paper, we do so using a new second-order cone representation of the Michaelis–Menten kinetics. The resulting model consists of linear stoichiometric constraints alongside second-order cone constraints that couple the reaction fluxes to metabolite concentrations. We formulate several new problems around this model: conic flux balance analysis, which augments flux balance analysis with metabolite concentrations; dynamic conic flux balance analysis; and finding minimal cut sets of networks with both reactions and metabolites. Solving these problems yields information about both fluxes and metabolite concentrations. They are second-order cone or mixed-integer second-order cone programs, which, while not as tractable as their linear counterparts, can nonetheless be solved at practical scales using existing software.

A deep dive into green infrastructure failures using fault tree analysis

Green Infrastructure has transformed traditional urban stormwater management systems by fostering a wide range of service functions. Despite their popularity, green infrastructure's performance can deteriorate over their lifecycle, leading to operational failures. The operation of green infrastructure has predominantly relied on reactive maintenance strategies. To anticipate malfunctions and enhance the performance of green infrastructure in the long run, failure data needs to be recorded so that deterioration processes and component vulnerabilities can be recognized, modelled and included in predictive maintenance schemes. This study investigates possible failures in representative GIs and provides insights into the most important events that should be prioritized in the data collection process. A method for qualitative Fault Tree Analysis using minimal cut sets are introduced, aiming to identify potential failures with the minimum number of events. To identify events of interest fault trees were constructed for bioswales, rain gardens and green roofs, for three groups of service function failures, namely runoff quantity control, runoff quality control and additional service functions. The resulting fault trees consisted of 45 intermediate and 54 basic events. The minimal cut set analysis identified recurring basic events that could affect operation among all three green infrastructure instances. These events are ‘trash accumulation’, ‘clogging due to sediment accumulation’, and ‘overly dense vegetation’. Among all the possible cut sets, events such as ‘plants not thriving’, ‘invasive plants taking over’, and ‘deterioration caused by external influences’ could potentially disrupt most of the service functions green infrastructure provides. Furthermore, the analysis of interactions between component failures shows vegetation and filter media layer failures have the highest influence over other components. The constructed fault trees and identified basic events could be potentially employed for additional research on data collection processes and calculating the failure rates of green infrastructure and as a result, contribute to a shift toward their proactive operation and maintenance.

Process Damping Identification Using Bayesian Learning and Time Domain Simulation

Abstract Process damping can provide improved machining productivity by increasing the stability limit at low spindle speeds. While the phenomenon is well known, experimental identification of process damping model parameters can limit pre-process parameter selection that leverages the potential increases in material removal rates. This paper proposes a physics-informed Bayesian method that can identify the cutting force and process damping model coefficients from a limited set of test cuts without requiring direct measurements of cutting force or vibration. The method uses time-domain simulation to incorporate process damping and provide a basis for test selection. New strategies for efficient sampling and dimensionality reduction are applied to lower computation time and minimize the effect of model error. The proposed method is demonstrated, and the identified cutting and damping force coefficients are compared to values obtained using machining tests and least-squares fitting.

Analisis Faktor Penyebab Keterlambatan Pelaksanaan Proyek Konstruksi pada CV. X Menggunakan Metode FTA

Delays that are not handled immediately can affect the quality of a company's performance. This research was conducted on a construction project undertaken by CV. X. Where 4 construction projects undertaken by CV. X as of January 2024 experienced delays in completion. For this reason, it is necessary to analyze the factors that cause delays in the project by using the FTA (Fault Tree Analysis) method which aims to find fault trees from various errors that will result in the occurrence of unwanted events. Data collection is done by survey using a questionnaire. The results of the questionnaire were then analyzed using the FTA Method and then determining the combination of these factors with a minimum cut set. Based on the results of the analysis using the FTA Method, there are 6 aspects that are factors causing delays, namely aspects of labor; material / equipment aspects; project implementation management aspects; contract aspects; aspects of work documents; aspects of natural conditions and force majors. With the largest probability value, namely the contract aspect of 0.21; project implementation management aspect of 0.18; material / equipment aspect of 0.15; aspects of natural conditions and force majors of 0.12; aspects of work documents of 0.09; aspects of labor of 0.08.

Dedekind Cuts, Moving Markers, and the Uncountability of ℝ

Summary We give short proofs that ℝ is uncountable directly from the definition of ℝ as the set of Dedekind cuts of ℚ .

Solving optimization problems with local light-shift encoding on Rydberg quantum annealers

We provide a non-unit-disk framework to solve combinatorial optimization problems such as maximum cut and maximum independent set on a Rydberg quantum annealer. Our setup consists of a many-body interacting Rydberg system where locally controllable light shifts are applied to individual qubits in order to map the graph problem onto the Ising spin model. Exploiting the flexibility that optical tweezers offer in terms of spatial arrangement, our numerical simulations implement the local-detuning protocol while globally driving the Rydberg annealer to the desired many-body ground state, which is also the solution to the optimization problem. Using optimal control methods, these solutions are obtained for prototype graphs with varying sizes at timescales well within the system lifetime and with approximation ratios close to one. The nonblockade approach facilitates the encoding of graph problems with specific topologies that can be realized in two-dimensional Rydberg configurations and is applicable to both unweighted as well as weighted graphs. A comparative analysis with fast simulated annealing is provided which highlights the advantages of our scheme in terms of system size, hardness of the graph, and the number of iterations required to converge to the solution. Published by the American Physical Society 2024

Logic programming-based Minimal Cut Sets reveal consortium-level therapeutic targets for chronic wound infections

Minimal Cut Sets (MCSs) identify sets of reactions which, when removed from a metabolic network, disable certain cellular functions. The traditional search for MCSs within genome-scale metabolic models (GSMMs) targets cellular growth, identifies reaction sets resulting in a lethal phenotype if disrupted, and retrieves a list of corresponding gene, mRNA, or enzyme targets. Using the dual link between MCSs and Elementary Flux Modes (EFMs), our logic programming-based tool aspefm was able to compute MCSs of any size from GSMMs in acceptable run times. The tool demonstrated better performance when computing large-sized MCSs than the mixed-integer linear programming methods. We applied the new MCSs methodology to a medically-relevant consortium model of two cross-feeding bacteria, Staphylococcus aureus and Pseudomonas aeruginosa. aspefm constraints were used to bias the computation of MCSs toward exchanged metabolites that could complement lethal phenotypes in individual species. We found that interspecies metabolite exchanges could play an essential role in rescuing single-species growth, for instance inosine could complement lethal reaction knock-outs in the purine synthesis, glycolysis, and pentose phosphate pathways of both bacteria. Finally, MCSs were used to derive a list of promising enzyme targets for consortium-level therapeutic applications that cannot be circumvented via interspecies metabolite exchange.

Situation modeling and evaluation for complex systems: A case study of satellite attitude control system

Because the dependence, competition, correlation, and other complex interactions among the components and between system and environment, the complex system is difficult to be modeled directly. As an important spacecraft system, satellite needs to meet the high reliability demand. However, the increasingly complex satellite system structure and complex space environment interference make the on-orbit satellite failure inevitable. Modeling and evaluation of satellite conditions can identify potential problems of key system components in time. Firstly, in order to accurately describe the system components and measure the reliability, the correlative damage coefficient is introduced to describe the component degradation process, and the component reliability model of the satellite attitude control system is established. Then, to solve the problem of large state space and changeable condition of system reliability model, the concept of tangent order pair is introduced into the multi-state dynamic fault tree of system. The depth-first search algorithm is used to sort the underlying event components of the fault tree, and the multi-state multi-value decision graph model of different state events is constructed. The minimum cut set of the model is obtained to evaluate the system state. The experimental results show that the proposed method can effectively simulate the system situation under different fault modes, effectively reduce the state space of the system reliability model, and improve the convergence and accuracy of the system fault probability evaluation.

Review and meta-analysis of the genetic Minimal Cut Set approach for gene essentiality prediction in cancer metabolism.

Cancer metabolism is a marvellously complex topic, in part, due to the reprogramming of its pathways to self-sustain the malignant phenotype in the disease, to the detriment of its healthy counterpart. Understanding these adjustments can provide novel targeted therapies that could disrupt and impair proliferation of cancerous cells. For this very purpose, genome-scale metabolic models (GEMs) have been developed, with Human1 being the most recent reconstruction of the human metabolism. Based on GEMs, we introduced the genetic Minimal Cut Set (gMCS) approach, an uncontextualized methodology that exploits the concepts of synthetic lethality to predict metabolic vulnerabilities in cancer. gMCSs define a set of genes whose knockout would render the cell unviable by disrupting an essential metabolic task in GEMs, thus, making cellular proliferation impossible. Here, we summarize the gMCS approach and review the current state of the methodology by performing a systematic meta-analysis based on two datasets of gene essentiality in cancer. First, we assess several thresholds and distinct methodologies for discerning highly and lowly expressed genes. Then, we address the premise that gMCSs of distinct length should have the same predictive power. Finally, we question the importance of a gene partaking in multiple gMCSs and analyze the importance of all the essential metabolic tasks defined in Human1. Our meta-analysis resulted in parameter evaluation to increase the predictive power for the gMCS approach, as well as a significant reduction of computation times by only selecting the crucial gMCS lengths, proposing the pertinency of particular parameters for the peak processing of gMCS.

Enhancing Safety in IoT Systems: A Model-Based Assessment of a Smart Irrigation System Using Fault Tree Analysis

The agricultural industry has the potential to undergo a revolutionary transformation with the use of Internet of Things (IoT) technology. Crop monitoring can be improved, waste reduced, and efficiency increased. However, there are risks associated with system failures that can lead to significant losses and food insecurity. Therefore, a proactive approach is necessary to ensure the effective safety assessment of new IoT systems before deployment. It is crucial to identify potential causes of failure and their severity from the conceptual design phase of the IoT system within smart agricultural ecosystems. This will help prevent such risks and ensure the safety of the system. This study examines the failure behaviour of IoT-based Smart Irrigation Systems (SIS) to identify potential causes of failure. This study proposes a comprehensive Model-Based Safety Analysis (MBSA) framework to model the failure behaviour of SIS and generate analysable safety artefacts of the system using System Modelling Language (SysML). The MBSA approach provides meticulousness to the analysis, supports model reuse, and makes the development of a Fault Tree Analysis (FTA) model easier, thereby reducing the inherent limitations of informal system analysis. The FTA model identifies component failures and their propagation, providing a detailed understanding of how individual component failures can lead to the overall failure of the SIS. This study offers valuable insights into the interconnectedness of various component failures by evaluating the SIS failure behaviour through the FTA model. This study generates multiple minimal cut sets, which provide actionable insights into designing dependable IoT-based SIS. This analysis identifies potential weak points in the design and provides a foundation for safety risk mitigation strategies. This study emphasises the significance of a systematic and model-driven approach to improving the dependability of IoT systems in agriculture, ensuring sustainable and safe implementation.

A characterisation of linear repetitivity for cut and project sets with general polytopal windows

The cut and project method is a central construction in the theory of Aperiodic Order for generating quasicrystals with pure point diffraction. Linear repetitivity (LR) is a form of ideal regularity of aperiodic patterns. Recently, Koivusalo and the present author characterised LR for cut and project sets with convex polytopal windows whose supporting hyperplanes are commensurate with the lattice, the weak homogeneity property. For such cut and project sets, we show that LR is equivalent to two properties. One is a low complexity condition, which may be determined from the cut and project data by calculating the ranks of the intersections of the projection of the lattice to the internal space with the subspaces parallel to the supporting hyperplanes of the window. The second condition is that the projection of the lattice to the internal space is Diophantine (or ‘badly approximable’), which loosely speaking means that the lattice points in the total space stay far from the physical space, relative to their norm. We review then extend these results to non-convex and disconnected polytopal windows, as well as windows with polytopal partitions producing cut and project sets of labelled points. Moreover, we obtain a complete characterisation of LR in the fully general case, where weak homogeneity is not assumed. Here, the Diophantine property must be replaced with an inhomogeneous analogue. We show that cut and project schemes with internal space isomorphic to Rn⊕G⊕Zr, for G finite Abelian, can, up to MLD equivalence, be reduced to ones with internal space Rn, so our results also cover cut and project sets of this form, such as the (generalised) Penrose tilings.

Computational analysis into the potential of azo dyes as a feedstock for actinorhodin biosynthesis in Pseudomonas putida.

Fermentation-based biosynthesis in synthetic biology relies heavily on sugar-derived feedstocks, a limited and carbon-intensive commodity. Unconventional feedstocks from less-noble sources such as waste are being utilized to produce high-value chemical products. Azo dyes, a major pollutant commonly discharged by food, textile, and pharmaceutical industries, present significant health and environmental risks. We explore the potential of engineering Pseudomonas putida KT2440 to utilize azo dyes as a substrate to produce a polyketide, actinorhodin (ACT). Using the constrained minimal cut sets (cMCS) approach, we identified metabolic interventions that optimize ACT biosynthesis and compare the growth-coupling solutions attainable on an azo dye compared to glucose. Our results predicted that azo dyes could perform better as a feedstock for ACT biosynthesis than glucose as it allowed growth-coupling regimes that are unfeasible with glucose and generated an 18.28% higher maximum ACT flux. By examining the flux distributions enabled in different carbon sources, we observed that carbon fluxes from aromatic compounds like azo dyes have a unique capability to leverage gluconeogenesis to support both growth and production of secondary metabolites that produce excess NADH. Carbon sources are commonly chosen based on the host organism, availability, cost, and environmental implications. We demonstrated that careful selection of carbon sources is also crucial to ensure that the resulting flux distribution is suitable for further metabolic engineering of microbial cell factories.

Risk assessment of fire and explosion accidents in oil-hydrogen fueling station based on fault tree analysis

Owing to the coexistence of gasoline, diesel, and hydrogen, a specific and thorough risk assessment approach must be promptly implemented for oil-hydrogen fueling stations. Consequently, this study makes innovations in considering the safety issues of the joint construction of gas station and hydrogen refueling station, and undertaking the modeling and analysis of fire and explosion incidents at oil-hydrogen fueling stations based on fault tree analysis. The findings reveal that the risk level for fire and explosion at oil-hydrogen fueling stations is categorized as grade III. In our fault tree analysis model, there are 3240 minimum cut sets and 194 minimum path sets. The probability of fire and explosion incidents occurring at oil-hydrogen fueling stations is calculated to be 0.000265. This indicates that while the possibility of such accidents is low, their potential severity is exceedingly high. Ultimately, it is recommended that the safety of oil-hydrogen fueling stations be enhanced through the reinforcement of emergency management, customer safety education, and electrostatic discharge devices.

An FE-MCS based modeling method for event sequence development for the multi-module probabilistic risk assessment

The Multi-Unit Probabilistic Risk Assessment (MUPRA) has been a research hotspot since the Fukushima Daichi accident in 2011. Among the technical elements, event sequence modeling is the most challenging one in MUPRA implementation. During the PRA development for a 600 MW HTR-PM nuclear power plant (HTR-PM600) which consist of 6 reactor modules, we find that the complexity of the obtained event sequence model increases rapidly and massively with the number of reactors, therefore it is quite difficult to guarantee the modeling correctness. To address this issue, in this paper, we propose a multi-module event sequence modeling method based on Minimal Cut Sets at the level of Functional Event (FE-MCS). The proposed method is illustrated with two cases in this paper, namely, Loss of Coolant Accident caused by the Isolatable Large breach in primary pressure boundary (LLOCA-I) and Loss of vital Direct Current bus (LSDC).

An Integrated Predictive Maintenance and Operations Scheduling Framework for Power Systems Under Failure Uncertainty

Maintenance planning plays a key role in power system operations under uncertainty as it helps providers and operators ensure a reliable and secure power grid. This paper studies a short-term condition-based integrated maintenance planning with operations scheduling problem while considering the possible unexpected failure of generators as well as transmission lines. We formulate this problem as a two-stage stochastic mixed-integer program with failure scenarios sampled from the sensor-driven remaining lifetime distributions of the individual system elements whereas a joint chance constraint consisting of Poisson Binomial random variables is introduced to account for failure risks. Because of its intractability, we develop a cutting-plane method to obtain an exact reformulation of the joint chance constraint by proposing a separation subroutine and deriving stronger cuts as part of this procedure. We also derive a second-order cone programming-based safe approximation of this constraint to solve large-scale instances. Furthermore, we propose a decomposition algorithm implemented in parallel fashion for solving the resulting stochastic program, which exploits the features of the integer L-shaped method and the special structure of the maintenance and operations scheduling problem to derive valid and stronger sets of optimality cuts. We further present preprocessing steps over transmission line flow constraints to identify redundancies. To illustrate the computational performance and efficiency of our algorithm compared with more conventional maintenance approaches, we design a computational study focusing on a weekly plan with daily maintenance and hourly operational decisions involving detailed unit commitment subproblems. Our computational results on various IEEE instances demonstrate the computational efficiency of the proposed approach with reliable and cost-effective maintenance and operational schedules. History: Accepted by Andrea Lodi, Area Editor for Design & Analysis of Algorithms—Discrete. Supplemental Material: The online appendix is available at https://doi.org/10.1287/ijoc.2022.0154 .

Bayesian update of fragility curves for equipment failure probability in seismic probabilistic safety assessment in nuclear power plant

The assessment of equipment failure probability during seismic probabilistic safety assessment (PSA) is a crucial and fundamental component in nuclear power plant seismic PSA. A Monte Carlo program must be formulated to compute the equipment failure probability and uncertainty analysis in seismic PSA. A numerical calculation method is employed for the integration calculation to obtain the conditional failure probability and the uncertainty distribution for each equipment. The fragility parameters of the equipment and the hazard data of the plant site serve as the input parameters for the failure probability of equipment in seismic PSA. In this study, the probability of occurrence of the minimal cut set in equipment failure under earthquake conditions is calculated using the Monte Carlo sampling method, and the uncertainty distribution for each peak ground acceleration interval is presented. Furthermore, we have updated the fragility parameters of the equipment through the utilization of the Bayesian approach. Based on the updated fragility curves, we have recalculated the failure probability of the equipment and compared it with the results before the update.